Magnetoencephalography-derived oscillatory microstate patterns across lifespan: the Cambridge centre for ageing and neuroscience cohort

Abstract The aging brain represents the primary risk factor for many neurodegenerative disorders. Whole-brain oscillations may contribute novel early biomarkers of aging. Here, we investigated the dynamic oscillatory neural activities across lifespan (from 18 to 88 years) using resting Magnetoencephalography (MEG) in a large cohort of 624 individuals. Our aim was to examine the patterns of oscillation microstates during the aging process. By using a machine-learning algorithm, we identify four typical clusters of microstate patterns across different age groups and different frequency bands: left-to-right topographic MS1, right-to-left topographic MS2, anterior-posterior MS3 and fronto-central MS4. We observed a decreased alpha duration and an increased alpha occurrence for sensory-related microstate patterns (MS1 & MS2). Accordingly, theta and beta changes from MS1 & MS2 may be related to motor decline that increased with age. Furthermore, voluntary ‘top-down’ saliency/attention networks may be reflected by the increased MS3 & MS4 alpha occurrence and complementary beta activities. The findings of this study advance our knowledge of how the aging brain shows dysfunctions in neural state transitions. By leveraging the identified microstate patterns, this study provides new insights into predicting healthy aging and the potential neuropsychiatric cognitive decline.


Introduction
The neural dynamic oscillatory patterns represent an early hallmark of aging. 1,2It has been posited that monitoring regular and predictable oscillations over the course of an adult' lifespan can aid in identifying potential progression of cognitive decline. 3,4At the cellular level, neurons have bio-electrochemical properties that facilitate the flow of electrical ions, resulting in the production of electromagnetic fields. 5There are five typical oscillatory brain signals in humans: delta, theta, alpha, beta and gamma.7][8] So far, the specific contribution of oscillatory changes within certain frequency bands on healthy aging are not yet well understood.
There are substantial changes in alpha oscillation during aging in humans, such as alpha slowing, 9 alpha power reduction, 10,11 alpha reactivity declining, 12,13 and alpha sub-component changes. 14Additionally, other oscillatory neurons and topology reflect different activities with the increasing age.For instance, Barry and De Blasio 15 observed reduced theta power and increased beta power, accompanied by decreased alpha power in elder adults.Furthermore, the neuropsychological Stroop task, a standard attention conflict measurement, highlights the opposing alpha and theta activities. 16hus, there is no consensus regarding whether aging involves multi-frequency dynamic oscillatory changes or is characterized by dominant alpha frequency deficiency.Previous Cam-CAN studies [17][18][19][20] have indicated reduced neural efficiency or specificity rather than compensation across lifespan.For example, Tibon et al. 17,18 showed that there was an age-related 'neural shift' with decreased occurrence of 'lower-order' networks in early visual states and increased occurrence of 'higher-order' fronto-temporal-parietal networks in visual and sensorimotor states.Another leading theory, 19,20 the posterior-to-anterior shift in aging (PASA), states that the anterior regions are recruited when posterior cortical function is impaired.
Koenig et al. 21defined four classes: A (left-to-right orientation), B (right-to-left orientation), C (anterior-posterior orientation) and D (fronto-central maxium).Brain sources underlying microstates in the literature showed that different microstates were highly correlated with functional magnetic imaging neural activities: auditory network (microstate A), visual network (microstate B), saliency network (microstate C) and attention network (microstate D). 22,23 Magnetoencephalography (MEG) is a non-invasive measurement of oscillatory magnetic fields with excellent temporal resolution and reasonable spatial resolution.In the present study, using resting MEG, we aimed to record the spontaneous rhythmic responses across lifespan and perform machine learning-based microstate clustering in a cohort study involving participants from different age groups.We hypothesized that alpha rhythm changes would be the most pronounced MEG microstate phenomenon in the aging brain.We further hypothesized that the microstates originating from the posterior regions would move anteriorly with age to increase the occurrence of higher-order sensory systems.

Demographics
The present study was based on the cohort of Cambridge Centre for Ageing and Neuroscience (Cam-CAN), involving 624 participants ranging from 18-88 years.The demographic details in the study are shown in Table 1.Participants were divided into five groups: young adults (YA, 18-29 years old), early middle-aged adults (EMA, 30-44 years old), late middle-aged adults (LMA, 45-59 years old), young seniors (YS, 60-74 years old) and elderly adults (EA, 75-88 years old).We performed resting MEG session for all these participants.The study was approved by the Cambridgeshire 2 Research Ethics Committee and all participants provided written informed consent prior to the study.

Resting MEG recordings
MEG data were recorded via 306-channel VectorView MEG system (Elekta Neuromag, Helsinki).MEG Vectorview system contains 204 planar gradiometers and 102 magnetometers.Magnetometers consist of a single coil to measure the magnetic flux perpendicular to the cortex surface.Planar gradiometers are arranged in pairs (a 'figure-of-eight' coil configuration) and the differences between two loops of the spatial gradient were calculated.The signals from planar gradiometers indicate magnetic fields from two directions in a plain parallel to the head surface.Participants were required to keep eyes closed but stay awake during resting MEG recording in a magnetically shielded room.MEG resting-state data were recorded with a duration of 8 and 40 sec, sampled at 1 kHz with a high-pass filter of 0.03 Hz.Head positions within MEG helmet were estimated via Head-Position Indicator (HPI) coils for offline correction of head movements.

Resting MEG data preprocess
The MaxFilter 2.2.12 software (Elekta Neuromag Oy, Helsinki, Finland) applied temporal Single Source Separation ('t-SSS') to preprocess continuous MEG data. 24,25T-SSS helps to detect and reconstruct noisy MEG channels, remove noises from external sources, correct headmotion artefacts and remove 50 Hz noise.Following the denoising steps, continuous MEG data were imported into MATLAB and resampled into 250 Hz via SPM12 (http:// www.fil.ion.ucl.ac.uk/spm).Then MEG data were filtered and segmented into different frequency bands: delta (1-3 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (14-30 Hz), gamma (31-40 Hz) and overall (1-40 Hz).We applied two-pass butter-worth filters, initially a high-pass filtre and then a lowpass filter with zero phase shift.Specifically, for delta, theta, alpha, beta, gamma and overall frequency bands, the high-pass filters were 1, 4, 8, 14, 31 and 1 Hz respectively; the low-pass filters were 3, 7, 13, 30, 40 and 40 Hz respectively.The filtered data for each frequency band were continuous datasets across experimental time.After that, we firstly combined original planar channels (i.e.MEGPLANAR electrodes) into MEGCOMB electrodes by using SPM12.Then original magnetometers (i.e.MEGMAG electrodes) and newly MEGCOMB electrodes were concatenated for better data interpretation in further microstates analyses.However, to compare the global explained variance for MEGCOMB electrodes with MEGMAG electrodes, we would re-run the preprocesses and separately conduct microstate global explained variance analyses for MEGCOMB electrodes or MEGMAG electrodes (Table 2).

Microstate pattern analyses using machine learning
For each oscillatory frequency band, the modification of the microstate k-means algorithm was used to extract microstate patterns. 26,27We applied + microstate (MATLAB package, https://github.com/lukewtait/microstate_toolbox)which had high signal-to-noise ratio for spontaneous transitions between brain states.The MEG microstate pipeline was based on machine-learning k-means pipeline presented by Pascual-Marqui et al. 28

Determination of microstate patterns
The observed topographic clusters were initially labeled by Koenig et al. (1999) as class A, B, C and D. 21,22 Microstate map A (MS1) and map B (MS2) exhibited a left-to-right orientation and a right-to-left orientation respectively.Microstate map C (MS3) indicated an anterior-posterior orientation while a fronto-central maximum was shown by microstate map D (MS4) (Fig. 1).As shown in Fig. 2, these four maps seem to consistently dominate the resting MEG data across different age groups and different frequency bands.As suggested by previous studies, 23,29,30 the polarity can be ignored in the microstate definition (blue versus.red in Fig. 1 & Fig. 2).For each microstate pattern, the correlations between the young group at overall frequency band (1-40 Hz) and other groups were calculated.The correlation results (mostly r > 0.90, Fig. 3) help to re-order the microstates patterns for different age groups and different frequency bands.Thus, the parameters of microstates across age group and oscillatory activities were comparable for a given microstate pattern after re-ordering.

Higher gradiometer global explained variance for dynamic microstate patterns
As reported in previous studies, the optimal number of clusters by using the machine-learning model (i.e.K-means clustering) is four microstate clusters.The GEV of four cluster maps explained varies among studies, ranging from 65% to 84%. 19ur findings indicated that gradiometer electrodes showed higher GEV compared to magnetometer electrodes for all frequency bands (Table 2).These observations might represent that the set of four cluster microstate patterns fit the common map for gradiometers rather than magnetometers.

Alpha duration decrease and occurrence increase for sensory networks
As mentioned above, MS1 and MS2 were indications of auditory and visual networks respectively.In our findings, there was significant decrease in alpha duration and increase in alpha occurrence for MS1 and MS2 (Fig. 4).In other words, aging adults showed decreased alpha duration time of stable MS1 or MS2 while there were increased number of times per second for the appearance of MS1 Furthermore, other frequency bands, such as theta band or beta band, showed accompanying decline with age for MS1 (Fig. 5).For example, the ANOVA and correlation results showed that theta duration and beta occurrence for MS1 were decreased across lifespan [Theta duration:  3 (details in Supplementary Table 1).
The possible aging-related compensatory changes of MS3 and MS4 were shown for beta band (Fig. 6).Interestingly, alpha and beta coverage was increased for MS3 while three beta parameters (coverage, occurrence, duration) were declined for MS4 [Alpha coverage:  3 (details in Supplementary Table 1).
Above all, there was dominant alpha deficiency with multi-rhythm decay across lifespan.We observed a wholebrain microstate patterns changes across lifespan.Microstate patterns for rhythmic activities showed a dominant alpha deficiency for most microstate patterns.Meanwhile, some other frequency bands had accompanying responses, such as theta and beta microstates patterns.These findings were briefly summarized in Table 4.

Discussion
In the present study, typical topographic clusters of microstates dominate the resting MEG data across different age groups and different frequency bands.The alpha deficiencies were apparent with increasing age, such as the whole-brain alpha occurrence abnormality.Apart from alpha occurrence changes, the decline of sensory and motor networks might be reflected via distributed oscillation microstate patterns.Specifically, we observed decreased alpha duration and increased alpha occurrence (left-to-right topographic MS1 & right-to-left topographic MS2) from young to elderly adults.Furthermore, theta duration and beta occurrence decreased across lifespan, which may be related to motor impairments.In addition, the 'top-down' voluntary network may be reflected by anterior-posterior MS3 and fronto-central MS4.
We found that the decreased beta was apparent for MS4 during aging while there may be complementary relationships between MS3 and MS4.

Machine-learning-identified microstate patterns with intra-subject stability
Traditionally oscillation analyses to extract power or peak frequency are meaningful, but the promising machinelearning-identified oscillatory microstates patterns may be superior to help observe the multi-frequency topology patterns with high temporal resolution.The concept of brain states is that a discrete microstate pattern remains stable before transitioning to a different state. 31,32Machine-learning microstate patterns rely on topologic clustering to label a couple of discrete clusters which can explain the majority of global variance across the cortex. 26,27Machine-learning k-means clustering was first proposed to calculate the global field power and to extract parameters (such as occurrence, duration, coverage) for each microstate pattern. 28Microstate analysis involves clustering the sensor-space spatial topographies without an arbitrary priori selected time window for continuous neural data.4][35][36] Our identified microstate patterns provide insights into transitions of spontaneous brain states across lifespan.
9][40] It was argued that four clusters in most previous studies exhibited highly similar topographies, strongly resembling the maps initially described by Koenig and colleagues. 21The oscillatory brain responses observed in microstates offer a valuable perspective for gaining a deeper understanding of aging.In consistent with previous studies, [21][22][23] we identified four clusters that were extracted across lifespan for different neural oscillations.
As for our findings on group-level correlations, the network activation patterns across lifespan describe intrasubject stability.The neural activities mainly include left-to-right, right-to-left, anterior-to-posterior and frontocentral topologies with healthy aging.In previous literature, four or five microstates revealed a set of brain regions active in the majority of networks. 41The GEV, a measure of how well the spatial microstate topographies can explain the variance of the data, can reach up to 84%. 23Regardless of the age group or frequency bands in our study, the common areas may correspond to the main hubs about structural/ functional brain networks (e.g.anterior and posterior cingulate cortices, dorsal superior prefrontal cortex, supramarginal gyrus, insula, precuneus, superior frontal cortex et al.). 42,43he patterns related to MS4 (i.e.fronto-central topology) are highly correlated across groups and frequencies.The MS4 has been attributed to the attentional network in the fMRI literature via source localization approach. 22Britz et al. demonstrated that MS4 correlated with negative BOLD activation in right-lateralized dorsal and ventral areas of the frontal and parietal cortices.Additionally, the  The units in the violin plots for duration and occurrence were 'second' and 'times' respectively.The bottom scatter plots were the correlations results between age and microstate parameters (duration or occurrence).Each data point represents the mean value per participant.The X axis of scatter plots were age and Y axis of scatter plots were mean value of microstate parameters (duration or occurrence).Correlation coefficients range from −1.0 to +1.0 with unit free.The uncorrected 'P < 0.01' values of Pearson's correlations were marked with the symbol '**'.The Bonferroni correction for Pearson's correlations was used but no significant correlations survived.The units in the scatter plots for age, duration and occurrence were 'years', 'second' and 'times' respectively.'MS1' and 'MS2' indicate 'microstate1' and 'microstate2' respectively in the figure .posterior cingulate cortices were active in all the microstates maps. 42,43Thus, we could probably see the important function of attention related to the stable fronto-central network across lifespan.

Microstate patterns with intra-subject variability
It is also interesting to find that there is still intra-subject variability in the maps between groups within a frequency or in the maps between frequencies within an age group.This may indicate the individualized microstate patterns and they won't be the exact map across age groups or across frequency bands.The MS1&MS2 variability appears at delta band and gamma band, indicating that the sensory impairments with the increase of age may be the coherence of the low frequency and high-frequency band.5][46] Previous researchers claimed that EEG delta-band phase and gamma-band amplitudes predict some complementary aspects of the time course of spikes of visual cortical neurons. 47Another possible explanation by Cam-CAN studies claimed that higher-order visual system may be more involved than lower-order visual system with healthy aging. 17,18These researches provide possible explanations for the delta and gamma intra-subject variability in our findings.Our findings may be consistent with previous Cam-CAN studies, indicating the recruitment of higherorder sensory system across lifespan.
The relative low group-level correlations for MS3 at theta and alpha bands may be related to the dysfunction in saliency network across lifespan.Existing literatures have showed that the salience network serves to identify salient stimuli and switch between the central executive network and the default-mode network. 48Older adults had lower Figure 5 Multi-frequency changes for MS1&MS2 across lifespan.Apart from dominant alpha oscillation decline across lifespan, there was accompanying decreased theta duration and decreased beta occurrence for MS1.Also, decreased alpha coverage was observed for MS2.The violin plots had age groups as X axis and mean value of microstate parameters (duration, occurrence or coverage) as Y axis.The black symbols '*'，'**', '***' indicated the post-hoc uncorrected significance level of 'P ≤ 0.05', 'P ≤ 0.01' and 'P ≤ 0.001' respectively.The red symbol of '***' indicated that the P-value survived after Bonferroni correction.The post hoc tests for MS1 beta occurrence and MS2 alpha coverage were analyzed via the Tukey method since there was significant homogeneity of variances; the post hoc tests for MS1 theta duration via the Tamhane's T2 since there was significant heterogeneity of variances.Five age groups were defined: young adults (YA, 18-29 years old), early middle-aged adults (EMA, 30-44 years old), late middle-aged adults (LMA, 45-59 years old), young seniors (YS, 60-74 years old) and elderly adults (EA, 75-88 years old).The units in the violin plots for duration, occurrence and coverage were 'second', 'times' and 'percentage' respectively.The bottom scatter plots were the correlations results between age and microstate parameters (duration, occurrence or coverage).Each data point represents the mean value per participant.The X axis of scatter plots were age and Y axis of scatter plots were mean value of microstate parameters (duration, occurrence or coverage).Correlation coefficients range from −1.0 to +1.0 with unit free.The uncorrected 'P < 0.01' values of Pearson's correlations were marked with the symbol '**'.The Bonferroni correction for Pearson's correlations was used but no significant correlations survived.The units in the scatter plots for age, duration, occurrence and coverage were 'years', 'second', 'times' and 'percentage' respectively.'MS1' and 'MS2' indicate 'microstate1' and 'microstate2' respectively in the figure .theta power in resting electroencephalograms and in task performances. 49Former study also suggested that alphaband oscillations play an important role in distractor filtering. 50Our current study supports the evidence that the theta/alpha may be a sensitive marker of cognitive aging in salience network.

Sensory microstate pattern effects across lifespan
In the current study, MS1 and MS2 indicated the dysfunction in visual and auditory networks, which probably reflect the alpha changes across lifespan.We found that the microstate patterns with decreased alpha duration and increased alpha occurrence are highly valuable in advancing our understanding of healthy aging.Most researchers favor the view that there is a strong relationship between levels of sensory and cognitive decline across lifespan. 51,52Age-related hearing loss and visual impairments starts to develop gradually in middle adulthood (around 35-45 years) and tends to accumulate over time. 53Furthermore, it has been demonstrated that older adults experience a decline in grey matter within the posterior temporal areas and parietal-occipital regions at an annual rate of 2%. 546][57][58][59] The alpha changes of MS1 and MS2 may be linked to the neuropsychiatric abnormality and neural dysfunctions, involving higher-order sensory systems.In our current study, the microstates in divergent coupling (i.e.delta, theta, gamma) oscillations for MS1 and MS2 may be related to motor dysfunction across lifespan.1][62][63] It has been demonstrated that motor cortical theta oscillation decline emerges in the medial frontal cortex with increased age.The co-activation of central-parietal regions and The units in the violin plots for occurrence and coverage were 'times' and 'percentage' respectively.The bottom scatter plots were the correlations results between age and microstate parameters (occurrence or coverage).Each data point represents the mean value per participant.The X axis of scatter plots were age and Y axis of scatter plots were mean value of microstate parameters (occurrence or coverage).Correlation coefficients range from −1.0 to +1.0 with unit free.The uncorrected 'P < 0.01' values of Pearson's correlations were marked with the symbol '**'.The Bonferroni correction for Pearson's correlations was used but no significant correlations survived.The units in the scatter plots for age, occurrence and coverage were 'years', 'times' and 'percentage' respectively.'MS3' and 'MS4' indicate 'microstate3' and 'microstate4' respectively in the figure .5][66][67] In addition to the involvement of alpha and beta oscillations in movement generation, [68][69][70][71] oscillatory theta activity also plays a role in movement production during cognitive control, action monitoring, et al. [72][73][74][75][76]

Saliency/attention network impairments during healthy aging
Our findings on MS3 & MS4 provide evidence for saliency/ attention network impairments from young to aging adults in support of PASA theory.The saliency network includes human brain composed of the anterior insular and dorsal anterior cingulate cortex.According to PASA theory, a neural shift from posterior to anterior is commonly detected.The modulations in alpha and beta oscillations at pre-central and postcentral cortical sites have been linked to execution or voluntary activities.8][79][80] Both alpha and beta power attenuation were reported to commence before voluntary movements in the fronto-medial and central areas. 81,82Furthermore, it was reported that elder adults exhibit more beta power during rest compared to young adults. 83Also, increased attenuation and recruitment of cortical areas occurred during self-paced movements in elders. 84In our study, the increased alpha occurrence/coverage and beta coverage in MS3 may be related to voluntary and execution declines across lifespan, moving anteriorly across lifespan.6][87] Our findings in MS3&MS4 may also indicate that low efficiency in neural activities appears with the increase of age.

Alpha dysfunction in cognitive decline across lifespan
Three parameters (duration, occurrence, coverage) in machinelearning based microstate patterns provide information with temporal and spatial resolution.Consistent with our hypotheses, microstate patterns indicated age-related whole-brain dynamic neural activities impairments with dominant alpha responses, accompanying with related frequency variations.Both topographic and temporal dynamic microstate changes are useful to explore and predict aging in humans.For example, as previously suggested, decreased alpha power and peak frequency amplitude on scalp level was typically found in occipital-parietal areas with the increased age. 12,88Alpha power was linked to a variety of cognitive decline, such as attention, inhibition and memory retrieval.However, in this study, the decreased alpha duration and increased alpha occurrence provide insights into the temporal modulations at left-right topographic orientation.

Strength and limitations
To sum up, there was several main findings according to this microstate analysis in the Cam-CAN cohort: 1) We found the dominant age-related alpha band microstate effects, especially an increased alpha occurrence in the whole brain with healthy aging.2) The MS1&MS2 intra-subject variability explained that individualized microstate patterns at different age groups involved a higher-order sensory system in elder adults.3) The microstate effects in MS3&MS4 implicate the dysfunction with healthy aging in saliency/attention network, moving anteriorly in elder adults.These findings provide insightful evidences for the reduced neural efficiency across lifespan.
The strength of this work is that we have identified novel biomarkers of microstate patterns and confirm that there are dominant alpha deficiencies with multi-frequency recession in the progression of aging.Furthermore, the Cam-CAN cohort provides a wide range of age groups to examine the progress of aging.The findings based on the gender-balanced cohort could convincingly reveal the oscillation changes with increased age.With high temporal and reasonable spatial resolution, both oscillations and topography across lifespan can be in-vivo detected.The limitation of the work is lack of tracking for the neural sources to uncover the relationships between microstate patterns and brain atrophy.In future work, it is essential to combine resting MEG, functional magnetic imaging resonance (MRI) and structural MRI to obtain multi-model aging biomarkers.

Conclusions
In conclusion, we discovered oscillation changes across different age groups via microstate patterns, and the results suggested that aging involve alpha microstate impairments, accompanying with theta and beta changes.The identified novel biomarker may be helpful to predict aging in future.

Figure 1
Figure 1 Determination of microstate patterns.MS1 and MS2 exhibited a left-to-right orientation and a right-to-left orientation respectively.MS3 indicated an anterior-posterior orientation while a fronto-central maximum was shown by MS4.The polarity was marked via blue and red: blue indicated positive and red was negative.The microstate patterns are group-level maps of young adults at 1-40 Hz. 'MS1', 'MS2', 'MS3' and 'MS4' indicate 'microstate1','microstate2','microstate3' and 'microstate4' respectively in the figure.The unit of microstate global map is 'fT'.

Figure 6
Figure 6 Alpha and Beta changes for MS3&MS4 across lifespan.The violin plots had age groups as X axis and mean value of microstate parameters (occurrence or coverage) as Y axis.The black symbols '*'，'**', '***' indicated the post hoc uncorrected significance level of 'P ≤ 0.05', 'P ≤ 0.01' and 'P ≤ 0.001' respectively.The red symbol of '***' indicated that the P-value survived after Bonferroni correction.The post hoc tests for MS3 beta coverage, MS3 alpha occurrence, MS4 alpha occurrence, MS4 beta coverage and MS4 beta occurrence were analyzed via the Tukey method since there was significant homogeneity of variances; the post hoc tests for MS3 alpha coverage via the Tamhane's T2 since there was significant heterogeneity of variances.Five age groups were defined: young adults (YA, 18-29 years old), early middle-aged adults (EMA, 30-44 years old), late middle-aged adults (LMA, 45-59 years old), young seniors (YS, 60-74 years old) and elderly adults (EA, 75-88 years old).The units in the violin plots for occurrence and coverage were 'times' and 'percentage' respectively.The bottom scatter plots were the correlations results between age and microstate parameters (occurrence or coverage).Each data point represents the mean value per participant.The X axis of scatter plots were age and Y axis of scatter plots were mean value of microstate parameters (occurrence or coverage).Correlation coefficients range from −1.0 to +1.0 with unit free.The uncorrected 'P < 0.01' values of Pearson's correlations were marked with the symbol '**'.The Bonferroni correction for Pearson's correlations was used but no significant correlations survived.The units in the scatter plots for age, occurrence and coverage were 'years', 'times' and 'percentage' respectively.'MS3' and 'MS4' indicate 'microstate3' and 'microstate4' respectively in the figure.

Table 1 Summary of participant demographics Group N Age (years) Mean Age (mean ± S.D.) Gender (female/male) Handedness (R/L)
aThe handedness record of one participant was ambidexter.

Table 2 Global explained variance for gradiometer only and magnetometer only electrodes
Visualizing microstate data via neuromag306 template.10.Global Explained Variance (GEV) comparison for gradiometer and magnetometer electrodes.For each frequency band, the GEV was computed for 'gradiometer only' and 'magnetometer only' respectively.Then paired-sample T-tests were used to compare the GEV differences for gradiometer and magnetometer electrodes per frequency band regardless of age groups.Chi-square tests were conducted for age differences per age group and handedness differences per age group.The demographic differences across age groups were analyzed by Chi-square tests.One-way ANOVA was used to compare the age-related group differences per frequency band per microstate pattern per microstate parameter (coverage, occurrence and duration).The post hoc tests were analyzed via the Tukey method if there was significant homogeneity of variances; Tamhane's T2 was used for multiple comparisons if there was significant heterogeneity of variances.The Pearson's correlations between age and each microstate parameter were analyzed per microstate pattern per frequency band.The Bonferroni method was used for multiple statistical correction of one-way ANOVA, post hoc tests and correlations.Multiple linear regression was analysed with age as a dependent variable and all other parameters as independent variables.Microstate GEV was compared via pair-sample T-tests between MEGCOMB and MEGMAG electrodes.The group-level correlations among microstate maps were based on the Pearson's correlations.